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EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMs

Yijie Li, Yuan Sun

TL;DR

EasyJudge is presented, a model developed to evaluate significant language model responses, featuring an intuitive visualization interface for ease of deployment and use and optimized with quantitative methods, which enables EasyJudge to run efficiently on consumer-grade GPUs or even CPUs.

Abstract

Recently, there has been a growing trend of employing large language models (LLMs) to judge the quality of other LLMs. Many studies have adopted closed-source models, mainly using GPT-4 as the evaluator. However, due to the closed-source nature of the GPT-4 model, employing it as an evaluator has resulted in issues including transparency, controllability, and cost-effectiveness. Some researchers have turned to using fine-tuned open-source LLMs as evaluators. However, existing open-source evaluation LLMs generally lack a user-friendly visualization tool, and they have not been optimized for accelerated model inference, which causes inconvenience for researchers with limited resources and those working across different fields. This paper presents EasyJudge, a model developed to evaluate significant language model responses. It is lightweight, precise, efficient, and user-friendly, featuring an intuitive visualization interface for ease of deployment and use. EasyJudge uses detailed datasets and refined prompts for model optimization, achieving strong consistency with human and proprietary model evaluations. The model optimized with quantitative methods enables EasyJudge to run efficiently on consumer-grade GPUs or even CPUs. We also provide detailed analysis and case studies to further reveal the potential of our method.

EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMs

TL;DR

EasyJudge is presented, a model developed to evaluate significant language model responses, featuring an intuitive visualization interface for ease of deployment and use and optimized with quantitative methods, which enables EasyJudge to run efficiently on consumer-grade GPUs or even CPUs.

Abstract

Recently, there has been a growing trend of employing large language models (LLMs) to judge the quality of other LLMs. Many studies have adopted closed-source models, mainly using GPT-4 as the evaluator. However, due to the closed-source nature of the GPT-4 model, employing it as an evaluator has resulted in issues including transparency, controllability, and cost-effectiveness. Some researchers have turned to using fine-tuned open-source LLMs as evaluators. However, existing open-source evaluation LLMs generally lack a user-friendly visualization tool, and they have not been optimized for accelerated model inference, which causes inconvenience for researchers with limited resources and those working across different fields. This paper presents EasyJudge, a model developed to evaluate significant language model responses. It is lightweight, precise, efficient, and user-friendly, featuring an intuitive visualization interface for ease of deployment and use. EasyJudge uses detailed datasets and refined prompts for model optimization, achieving strong consistency with human and proprietary model evaluations. The model optimized with quantitative methods enables EasyJudge to run efficiently on consumer-grade GPUs or even CPUs. We also provide detailed analysis and case studies to further reveal the potential of our method.

Paper Structure

This paper contains 20 sections, 10 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Evaluation Based on Reference Texts
  4. LLM-Based Text Evaluation
  5. System Overview
  6. Data Processing Module
  7. Evaluation Model Training
  8. User-Friendly Interface
  9. Implementation Details
  10. Data Processing
  11. Definition of Evaluation Scenarios and Criteria
  12. Dataset Construction
  13. Evaluation Model Fine-Tuning
  14. User-Friendly Interface Development
  15. Demonstration Scenarios
  16. ...and 5 more sections

Figures (10)

  • Figure 1: Overview of the EasyJudge method.
  • Figure 2: A Screenshot of EasyJudge with an example evaluation task of PAIRWISE.
  • Figure 3: The prompt for invoking GPT-4 extended instructions.
  • Figure 4: The prompt used for PAIRWISE instruction evaluation.
  • Figure 5: The prompt used for POINTWISE instruction evaluation.
  • ...and 5 more figures